Research on an alternative method of turbine motor signal

In the modern mortar radio fuze, the use of turbine generators as the power source for fuzes is very common. The ballistic air pressure during the flight of the projectiles is used as the driving force to drive the turbine motor. In this paper, the turbine motor signal is parameterized in combination with the actual situation, and the idea of using the hardware to simulate the turbine power generation is proposed. The generation of the turbine motor signal is simulated by means of simulation software. Design the circuit to verify the simulation results, and have a certain reference for how to easily detect the fuze in the mass production process

Bis[3-(2-carboxy­ethen­yl)pyridinium-1-acetato]dichloridozinc(II)

In the title complex, [ZnCl2(C10H9NO4)2], the ZnII ion lies on a twofold rotation axis and is four-coordinated by two carboxyl­ate O atoms from two 3-(2-carboxy­ethen­yl)pyridinium-1-acetate ligands in a monodentate mode and two Cl atoms in a distorted tetra­hedral geometry. In the crystal structure, inter­molecular O—H⋯O hydrogen bonds link the mol­ecules into a double-chain structure extending parallel to [101]

Profitable Retail Customer Identification Based on a Combined Prediction Strategy of Customer Lifetime Value

As a fundamental concept of customer relationship management, customer lifetime value (CLV) serves as a crucial metric to identify profitable retail customers. Various methods are available to predict CLV in different contexts. With the development of consumer big data, modern statistics and machine learning algorithms have been gradually adopted in CLV modeling. We introduce two machine learning algorithms—the gradient boosting decision tree (GBDT) and the random forest (RF)—in retail customer CLV modeling and compare their predictive performance with two classical models—the Pareto/NBD (HB) and the Pareto/GGG. To ensure CLV prediction and customer identification robustness, we combined the predictions of the four models to determine which customers are the most—or least—profitable. Using 43 weeks of customer transaction data from a large retailer in China, we predicted customer value in the future 20 weeks. The results show that the predictive performance of GBDT and RF is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Because the predictions are not entirely consistent, we combine them to identify profitable and unprofitable customers

A Cross-Cultural Perspective on the Preference for Potential Effect: An Individual Participant Data (IPD) Meta-Analysis Approach

A recent paper [Tormala ZL, Jia JS, Norton MI (2012). The preference for potential. Journal of personality and social psychology, 103:567-583] demonstrated that persons often prefer potential rather than achievement when evaluating others, because information regarding potential evokes greater interest and processing, resulting in more favorable evaluations. This research aimed to expand on this finding by asking two questions: (a) Is the preference for potential effect replicable in other cultures? (b) Is there any other mechanism that accounts for this preference for potential? To answer these two questions, we replicated Tormala et al.'s study in multiple cities (17 studies with 1,128 participants) in China using an individual participant data (IPD) meta-analysis approach to test our hypothesis. Our results showed that the preference for potential effect found in the US is also robust in China. Moreover, we also found a pro-youth bias behind the preference for potential effect. To be specific, persons prefer a potential-oriented applicant rather than an achievement-oriented applicant, partially because they believe that the former is younger than the latter